Estimating Biomass of Eastern Redcedar (Juniperus virginiana) with Key
Variables and Understory Analysis
Natural Resources and Environmental Sciences Capstone Project
Kansas State University
Advisor: Professor Kevin Price
05/09/2013
Tyler Bletscher, Chelsea Corkins, Rachel Enns, John Jones, Michael Kaiser, Matti Kuykendall, Debbie
Mildfelt, Jake Rockwell, Paul Waters, Mike Weber, Thomas Weninger, Danielle Williams
1|Page
Introduction
With increasing awareness of the invasion of Juniperus virginiana, or Eastern Redcedar,
across much of the Great Plains (Bragg and Hulbert 1976), it has become more necessary to
determine the implications of this population increase. With a large amount of this
encroachment happening within the past 30 years (Pierce and Reich 2009) there are many data
points which need to be collected to find conclusive results of the growing populations. The
areas that are dominated by the Eastern Redcedar are commonly found to be of poor soil
nutritional value, steep slope and of a shallow soil profile (Pierce and Reich 2009).
Alternative fuels are in constant demand throughout the world. The possibility of using
Eastern Redcedar as a potential biofuel source to supply this demand requires many variables
(i.e. logistics, consistent economic demand, and total physical supply) that must be closely
analyzed before a conclusion can be found. This group decided to focus on predicting the
biomass of Eastern Redcedars within varied tree densities. The goal was to determine the best
variables for predicting the biomass of a single redcedar tree, and to also determine what effect
this has on the tree’s understory.
2|Page
Literature Review
Eastern Redcedar, Juniperus virginiana, has not been abundant in the tallgrass prairie
until recent decades. In the past 50 years, some study areas have transitioned from completely
treeless to being defined by hardwood growth (Fitch et al 2001). This new invasion, mainly of
Juniperus virginiana, has occurred due to several factors; the practice of fire suppression, the
decline of large grazing mammal populations, and change in land use (Norris et al 2001).
Disruptions in the ecosystem, such as fire, have played an important role in plant associations on
the prairie. Fire suppression has allowed the non-native species of Juniper, Eastern Redcedar, to
invade. The trees’ fire-intolerant seeds have had a significant impact on the prairie community
including altering species habitat, soil chemistry, and biodiversity (Pierce and Peter 2008). A
wide variety of small mammals are essential to the tallgrass prairie system for adjusting a plant
community’s composition and structure. Since the invasion of redcedar, the configuration of the
understory has changed, showing a decrease in the diversity of these mammals (Horncastle et al
2005). Along with the habitat change associated with the encroachment of Eastern Redcedar, the
soil chemistry changed dramatically. Tallgrass prairie biomass is concentrated belowground but
is highly productive aboveground, with the change to redcedar stands, the biomass is
concentrated aboveground, allowing more storage of carbon. With the increase of carbon
storage, the tallgrass prairie and its associated plant communities can alter many components
such as decay, growth, and productivity (Norris et al 2001).
Eastern Redcedar has invaded and established a foot hold in Kansas and across the
Midwest over time. For example in Oklahoma, “The Governor’s Task Force, in 2002, suggested
that some 8 million acres of juniper existed in the state. That same Task Force estimated that
Oklahoma is losing almost 300,000 acres per year to juniper encroachment” (McKinley, 2012).
3|Page
This diagnosis is true across much of the native tallgrass prairie, illustrating an increasing
demand for a solution to stopping the spread of Eastern Redcedar. Research has been done on the
potential biofuel use of redcedar through field studies and biomass estimates. By collecting field
data such as DBH (diameter at breast height), DGL (diameter at ground line), canopy width and
tree height, each tree’s biomass can be estimated. With this data and an estimate of the total
acreage of redcedar in Kansas, one can predict the total potential energy resource available in
Kansas. However this in no way implies all the biomass is accessible, available, or economical to
harvest, transport, process or utilize for biofuel purposes. This value potentially contained within
Eastern Redcedar presents an opportunity. Although Eastern Redcedar has already invaded,
recovery of the native ecosystem after removal is viable (Pierce et al 2010). This represents a
crucial link for this topic. The removal of Juniperus virginiana could be both economically and
environmentally advantageous. The removal of Eastern Redcedar for its value as biofuel could
positively impact the local ecology by helping restore populations of native fauna (Alford et al
2012).
This research illustrates both the problems and opportunities associated with Eastern
Redcedar invasion. Redcedar has been shown to cause environmental problems, but the
environment has also shown resiliency after removal of the species. This resiliency paired with
the potential value of the biomass contained in Eastern Redcedar provides an opportunity to
restore the local ecosystem. What is needed, however, is more research on the reliability of
variables and methods to estimate the amount of biomass available.
Research question and hypothesis
Biomass- Which of the key variables is the best indicator of redcedar biomass?
Understory- There is no relationship between canopy cover and vegetation composition.
4|Page
Study Area
For this study, an area located 11.5 miles Northwest of Kansas State University on Tuttle
Creek Blvd. Figure 1 is a map showing Kansas State University as the starting location (A) and
the study area (B) as the final destination point was utilized. Tuttle Creek Reservoir is the large
body of water located in the Northeastern portion of the photo.
Figure 1: The image above is showing Kansas State University (A) and the study area (B). The blue line shows driving directions.
The name of the study area is The Prairie Rose Ranch owned by Mr. Charles Hall. This
ranch has a large population of Eastern Redcedar, and was selected for this reason. Figure 2 is an
image showing an aerial view of the entirety of The Prairie Rose Ranch, highlighting the amount
of tree cover in the area. This image shows the tree density gradient at the ranch, with areas of
low, medium, and high density cover.
5|Page
Figure 2: The above image is an aerial image showing the entirety of the study area.
Three transects were plotted within the Juniperus virginiana forest to study. Each
transect was 200 ft. long and was plotted in a specific tree density. Figure 3 shows the three
different transects. The pink pins represent the low density forest, yellow pins represent the
medium density forest, and the blue pins represent the high density forest.
Figure 3: The above image is an aerial image showing the locations of the three transects.
6|Page
The Prairie Rose Ranch is located in the Flint Hills of Kansas within the tallgrass prairie
ecoregion. The Flint Hills are comprised of primarily Permian limestone and shale. The
tallgrass prairie is comprised of switchgrass, big bluestem, little bluestem, and Indiangrass.
Within the Flint Hills the land use is primarily rangeland for cattle grazing and there are areas of
cropland agriculture around river valleys (Hansen, 2012).
Charles Hall bought the area in 1977, mostly because of the trees. He has not done
anything to the land since 1977, and there hasn’t been farming on the land since 1960. This
information has been gathered from a personal interview with Charles Hall himself. Figure 4 is a
picture of the study area in 1962, with a much smaller population of trees than at present.
Figure 4: The above image is an aerial image for the study area in 1962. The red box outlines the study area.
7|Page
Field Methods
Transects
To obtain data from the redcedar trees for the purpose of this study, the placement of
three transect was chosen; one each in three different tree densities. The densities were chosen
by looking at aerial photos and approximating the densities of trees to match levels of high,
medium, and low. Figure 5 shows pictures of the different densities. Once the three densities
were found, 60m of rope was laid out for each transect. All three transects ran north to south,
with seven points marked on each rope every 10m. In total, 21 points were marked out over
three transects. Following the setup of transects, data was then able to be collected. At each
point there were four quadrants labeled quadrant one through four. The tree closest to each
quadrant was chosen to collect data from. Once a tree was chosen, data was taken from it.
Figure 5: Tree densities (low, medium, high)
8|Page
Figure 6. To the left is a diagram of an example of a
transect laid out. The red line is a transect and the blue
dots represent the plots. Plot number 1 is the furthest
north blue dot, plot 7 is the furthest south plot.
N
Plots
Transect
Figure 7: The diagram below shows how the
quadrants are identified for each of the 21 points.
Quadrant 4
Quadrant 3
Quadrant 1
Quadrant 2
N
9|Page
Biomass
The measurements that were gathered from each tree were diameter at ground line,
diameter at breast height, distance from center of plot, canopy width (two directions), and tree
height. In addition, some trees were cored and five trees were cut down and weighed. Gathering
the data for diameter at ground line (Figure 8), diameter at breast height, canopy width (two
directions), and distance from center of plot (Figure 9) was all done with a measuring tape. The
tree height was calculated with a Clinometer (Figure 10) and cores were taken with a tree corer.
Within the medium tree density transect, five trees were chosen to cut down and weigh; two trees
were picked from plot 1, two trees were picked from plot 4, and one tree was picked from plot 7.
Figures 8,9 and 10
A field form was produced to try and keep data as accurate and consistent as possible.
Figure 11 is an example of the field form that was developed for this project. This particular field
form was for medium density. In the field it was easier to take the circumference measurements
than derive the diameter from the circumference measurements. In the analysis, the diameter was
used but it was derived from the circumference.
10 | P a g e
Transcet 2 (Medium Density)
Circumference Ground Line
Circumference Breast Height Distance from Center of Plot Canopy Width (2 directions) Tree Height
Age
Plot 1
Quad 1
Quad 2
Quad 3
Quad 4
Plot 2
Quad 1
Quad 2
Quad 3
Quad 4
Plot 3
Quad 1
Quad 2
Quad 3
Quad 4
Plot 4
Quad 1
Quad 2
Quad 3
Quad 4
Plot 5
Quad 1
Quad 2
Quad 3
Quad 4
Plot 6
Quad 1
Quad 2
Quad 3
Quad 4
Plot 7
Quad 1
Quad 2
Quad 3
Quad 4
Figure 11: The table above shows the field form used for medium density.
11 | P a g e
Soil Sampling
One soil composite sample was taken for each quadrant using a standard hand-held soil
probe (Figure 12). Each composite was comprised of three samples taken from 0-3”, sampled
equidistant and diagonally from the quadrant origin. The 21 soil samples were placed in an oven
at 60 degrees Celsius overnight, then ground and passed through a 2mm sieve. 10 gram samples
were then weighed and 10mL of water was added. The pHs were then read using the Kansas
State University Soil Testing Laboratory robotic soil probe, the Skalar SP50 (Figure 13).
Figures 12 & 13
12 | P a g e
Understory Cover
Starting from the north end of the transect and moving along in intervals of 30 feet the
Daubenmire Quadrat method was used. At each point along the transect, three sticks were used
to construct the Daubenmire square, 2 perpendicular from the transect and 1 parallel to the
transect that connected the 2 perpendicular sticks. After the Daubenmire quadrat was laid out,
visual estimates (in percentages) of certain life forms and features were taken. This includes
grasses, forbs, shrubs and vines, bare ground, litter, persistent litter, rock, and moss on a scale of
1 – 5%, 6 – 25%, 26 -50%, 51 - 75%, 76 – 95%, and 96 – 100% (Figure 14). This method was
used on all three density transects.
Figure 14
13 | P a g e
Canopy Cover
For this data collection, a Standard Model-C Densiometer (Figure 15) was used to
measure the amount of canopy cover at each location. As with many of the other collection
methods, each transect was approached with having 7 points, each 10 meters apart. Each point
was then split into four quadrants to allow for more accurate density measurements.
Figure 15
Within each quadrant, the general directions on the densiometer were followed (Figure 16). This
included holding the tool 12-18 inches in front of the observer’s body at elbow height. The
operator’s body should be just outside of the reflective mirror (Figure 17).
14 | P a g e
Figures 16 &17
Within each square etched into the mirror, four equi-spaced dots are visualized and the
number of dots covered by canopy openings (i.e. open sky) were counted. This final number is
then multiplied by 1.04 to account for the lack of four dots on the densiometer. This result is the
overhead space not occupied by canopy, so to calculate the canopy cover, find the difference
between this overhead space and 100. This process was repeated in all four cardinal directions
(North, East, South, and West) and in each of the four quadrants at each point for a total of 16
measurements per point. These methods were continued for each point in all three density
transects.
15 | P a g e
Worm Observations
For this data collection, preparation needed to occur before a visit to the study site. The
mixture needed required 1/2 canister of dry mustard to 1 gallon of water (Figures 18 and 19). To
simplify this process, one canister of mustard was mixed with water in a glass, then 1/2 the
mixture would be poured into 1 gallon nearly full of water. One gallon was created for each point,
resulting in 21 different gallons.
Figures 18 & 19
Once in the field, the grass and turf in each southeast quadrant was removed by hand. The soil in
this section was then soaked with approximately 1/3 gallon of the mustard mixture (shaken first).
Once this amount was soaked into the soil without running off, the actions were repeated until
the entire gallon was used (Figure 20).
16 | P a g e
Figures 20 & 21
Once all of the liquid had been poured on the soil, the number of biotic creatures that appeared
was counted immediately. Items such as snails, worms (Figure 21), spider, etc, were all split into
separate categories and recorded in the field.
17 | P a g e
Data and Results
Numerical Data
Figure 22 is the filled out field form for the medium density transect, with no cores taken
in the medium density. Figure 23 is the filled out field form for the low density transect.
Transcet 2 (Medium Density)
Circumference Ground Line
Circumference Breast Height Distance from Center of Plot Canopy Width (2 directions) Tree Height
Plot 1
Quad 1
Quad 2
Quad 3
Quad 4
30 in.
17 in.
18 in.
40 in.
20 in.
9 in.
12 in.
27 in.
15 ft.
23 ft. 4 in.
35 ft. 10 in.
16 ft. 7 in.
14 ft. 1in. / 15 ft. 10 in.
7 ft. 10 in. / 8 ft. 5 in.
10 ft. 2in. /
18 ft. / 19 ft. 4 in.
6.4m
3.2m
3.6m
5.6m
Plot 4
Quad 1
Quad 2
Quad 3
Quad 4
37 in.
16 in.
19 in.
39 in.
27 in.
6 in.
11 in.
23 in.
11 ft.
8 ft. 11 in.
16 ft.
22 ft.
14 ft. 3 in. / 12 ft. 6 in.
8 ft. 7 in. / 9 ft. 6 in.
8 ft. 8in. / 10 ft 9 in.
13 ft. 9 in. / 11 ft. 11 in.
5.2m
3.6m
4.4m
5.6m
Plot 7
Quad 1
Quad 2
Quad 3
Quad 4
41 in.
52 in.
22 in.
11 in.
52 in.
33 in.
16 in.
20 in.
30 ft.
15 ft. 9 in.
15 ft. 8 in.
18 ft. 5 in.
21 ft 3 in. / 25 ft. 7 in.
12 ft 1 in. / 18 ft. 8 in.
8 ft 8 in. / 16 ft. 2 in.
10 ft. 7 in. / 8 ft. 4 in.
7.6m
7.6m
4.8m
3.6m
Age
Figure 22: The table above is the medium density field form.
Transcet 1 (Low Density)
Circumference Ground Line
Circumference Breast Height Distance from Center of Plot Canopy Width (2 directions) Tree Height
Plot 1
Quad 1
Quad 2
Quad 3
Quad 4
1.43
0.9
0.9
1.22
1.08
0.59
0.8
0.63
7.6
13.65
22.9
5
8.25/8.7
4.1/4.38
7.6/7.65
6.3/6.2
6.54
7.23
6.64
5.7
Plot 2
Quad 1
Quad 2
Quad 3
Quad 4
1.35
0.37
0.62
0.42
0.85
0.21
0.4
0.25
10.8
3.47
14
7.2
8.4/7.25
3.5/3.1
3.5/4.2
2.3/3
6.8
3.3
4.2
4.39
Plot 3
Quad 1
Quad 2
Quad 3
Quad 4
1.35
1.3
0.78
0.82
1
0.56
0.53
0.36
9
24.23
5.45
5.95
9.9/7.05
7.3/7.1
4.4/3.5
5.8/6
6.12
6.54
5.78
5
Age
Figure 23: The table above is the low density field form.
18 | P a g e
ID
pH
Low 1- North end
Low 2
Low 3
Low 4 @ red flag
7.7
7.9
8
8.1
Low 5
Low 6
Low 7- South end
8
8
7.9
Medium 1- North
end
Medium 2
Medium 3
Medium 4 @ red
flag
Medium 5
Medium 6
Medium 7- South
end
High 1-North end
High 2
High 3
HIgh 4 @ red flag
High 5
HIgh 6
High 7- South end
Low avg pH=7.94
Med. avg pH=7.94
High avg pH=7.86
Conclusion: density does not
drastically effect soil pH
8
8
8
8
7.8
7.9
7.9
7.8
8
7.8
7.9
7.8
7.8
7.9
Figure 24: The above table shows the pH of the soils from all three transects.
19 | P a g e
Transect:
Low Density
4/12/2013
Point 1:
N
Northern Most E
S
W
Reported By: Chelsea Corkins and Terrance Crossland
Quadrant
Clear Sky
96
96
90
87
NW
Tree
0
0
6
9
Tree Adj Clear Sky
0
96
0
96
6.24
96
9.36
90
NE
Tree
0
0
0
6
Tree Adj Clear Sky
0
96
0
96
0
91
6.24
57
SE
Tree
0
0
5
39
Tree Adj Clear Sky
0
96
0
96
5.2
37
40.56
38
SW
Tree
0
0
59
58
Tree Adj
0
0
61.36
60.32
Point 2:
N
E
S
W
86
50
96
80
10
46
0
16
10.4
47.84
0
16.64
44
1
82
93
52
95
14
3
54.08
98.8
14.56
3.12
52
80
96
96
44
16
0
0
45.76
16.64
0
0
95
88
91
91
1
8
5
5
1.04
8.32
5.2
5.2
Point 3:
N
E
S
W
96
56
92
96
0
40
4
0
0
41.6
4.16
0
10
0
72
94
86
96
24
2
89.44
99.84
24.96
2.08
27
76
79
96
69
20
17
0
71.76
20.8
17.68
0
91
79
94
96
5
17
2
0
5.2
17.68
2.08
0
Point 4:
N
E
S
W
88
96
96
96
8
0
0
0
8.32
0
0
0
96
75
96
96
0
21
0
0
0
21.84
0
0
96
96
79
96
0
0
17
0
0
0
17.68
0
90
96
96
96
6
0
0
0
6.24
0
0
0
Point 5:
N
E
S
W
96
84
80
81
0
12
16
15
0
12.48
16.64
15.6
62
80
96
91
34
16
0
5
35.36
16.64
0
5.2
92
64
96
56
4
32
0
40
4.16
33.28
0
41.6
96
79
31
12
0
17
65
84
0
17.68
67.6
87.36
Point 6:
N
E
S
W
96
96
96
96
0
0
0
0
0
0
0
0
92
96
96
96
4
0
0
0
4.16
0
0
0
96
95
96
94
0
1
0
2
0
1.04
0
2.08
96
96
96
96
0
0
0
0
0
0
0
0
Point 7:
N
Southern Most E
S
W
3
12
0
0
93
84
96
96
96.72
87.36
99.84
99.84
3
12
0
0
93
84
96
96
96.72
87.36
99.84
99.84
86
84
96
76
10
12
0
20
10.4
12.48
0
20.8
1
85
10
77
95
11
86
19
98.8
11.44
89.44
19.76
Figure 25: Canopy Cover Percentage along Low Density Transect.
20 | P a g e
1.04
Transect:
Medium Density
4/12/2013
Reported By: Chelsea Corkins and Terrance Crossland
Quadrant
NW
Point 1:
N
Northern Most E
S
W
Clear Sky
96
91
96
96
NE
Tree
0
5
0
0
Tree Adj Clear Sky
0
87
5.2
78
0
96
0
96
SE
Tree
9
18
0
0
Tree Adj Clear Sky
9.36
96
18.72
87
0
92
0
96
SW
Tree
0
9
4
0
Tree Adj Clear Sky
0
96
9.36
96
4.16
96
0
96
Tree
0
0
0
0
Tree Adj
0
0
0
0
Point 2:
N
E
S
W
88
14
92
77
8
82
4
19
8.32
85.28
4.16
19.76
32
0
0
12
64
96
96
84
66.56
99.84
99.84
87.36
12
11
66
95
84
85
30
1
87.36
88.4
31.2
1.04
95
77
96
96
1
19
0
0
1.04
19.76
0
0
Point 3:
N
E
S
W
0
17
13
0
96
79
83
96
99.84
82.16
86.32
99.84
1
12
48
0
95
84
48
96
98.8
87.36
49.92
99.84
52
50
11
40
44
46
85
56
45.76
47.84
88.4
58.24
6
67
68
10
90
29
28
86
93.6
30.16
29.12
89.44
Point 4:
N
E
S
W
96
96
94
96
0
0
2
0
0
0
2.08
0
96
95
77
96
0
1
19
0
0
1.04
19.76
0
96
6
54
96
0
90
42
0
0
93.6
43.68
0
96
50
17
96
0
46
79
0
0
47.84
82.16
0
Point 5:
N
E
S
W
5
14
15
0
91
82
81
96
94.64
85.28
84.24
99.84
32
94
96
0
64
2
0
96
66.56
2.08
0
99.84
90
34
0
19
6
62
96
77
6.24
64.48
99.84
80.08
12
59
16
9
84
37
80
87
87.36
38.48
83.2
90.48
Point 6:
N
E
S
W
88
77
94
70
8
19
2
26
8.32
19.76
2.08
27.04
69
49
93
91
27
47
3
5
28.08
48.88
3.12
5.2
95
29
84
92
1
67
12
4
1.04
69.68
12.48
4.16
92
84
72
69
4
12
24
27
4.16
12.48
24.96
28.08
Point 7:
N
Southern Most E
S
W
92
86
12
36
4
10
84
60
4.16
10.4
87.36
62.4
72
82
8
58
24
14
88
38
24.96
14.56
91.52
39.52
75
80
7
22
21
16
89
74
21.84
16.64
92.56
76.96
92
35
5
11
4
61
91
85
4.16
63.44
94.64
88.4
Figure 26: Canopy Cover Percentage along Medium Density Transect.
21 | P a g e
1.04
Transect:
High Density
4/12/2013
Point 1:
N
Northern Most E
S
W
Reported By: Chelsea Corkins and Terrance Crossland
Quadrant
Clear Sky
94
74
77
83
NW
Tree
2
22
19
13
Tree Adj Clear Sky
2.08
96
22.88
8
19.76
15
13.52
91
NE
Tree
0
88
81
5
Tree Adj Clear Sky
0
73
91.52
2
84.24
0
5.2
75
SE
Tree
23
94
96
21
Tree Adj Clear Sky
23.92
93
97.76
17
99.84
18
21.84
96
SW
Tree
3
79
78
0
Tree Adj
3.12
82.16
81.12
0
Point 2:
N
E
S
W
1
0
6
13
95
96
90
83
98.8
99.84
93.6
86.32
0
0
1
8
96
96
95
88
99.84
99.84
98.8
91.52
1
24
96
18
95
72
0
78
98.8
74.88
0
81.12
16
33
7
6
80
63
89
90
83.2
65.52
92.56
93.6
Point 3:
N
E
S
W
0
25
1
1
96
71
95
95
99.84
73.84
98.8
98.8
56
72
21
30
40
24
75
66
41.6
24.96
78
68.64
14
10
5
1
82
86
91
95
85.28
89.44
94.64
98.8
9
45
18
0
87
51
78
96
90.48
53.04
81.12
99.84
Point 4:
N
E
S
W
0
4
8
0
96
92
88
96
99.84
95.68
91.52
99.84
0
0
0
5
96
96
96
91
99.84
99.84
99.84
94.64
0
3
10
3
96
93
86
93
99.84
96.72
89.44
96.72
4
4
0
6
92
92
96
90
95.68
95.68
99.84
93.6
Point 5:
N
E
S
W
4
0
0
26
92
96
96
70
95.68
99.84
99.84
72.8
0
0
0
3
96
96
96
93
99.84
99.84
99.84
96.72
6
6
1
7
90
90
95
89
93.6
93.6
98.8
92.56
23
2
0
11
73
94
96
85
75.92
97.76
99.84
88.4
Point 6:
N
E
S
W
23
0
81
0
73
96
15
96
75.92
99.84
15.6
99.84
0
0
85
7
96
96
11
89
99.84
99.84
11.44
92.56
19
15
66
25
77
81
30
71
80.08
84.24
31.2
73.84
76
31
51
13
20
65
45
83
20.8
67.6
46.8
86.32
Point 7:
N
Southern Most E
S
W
0
6
16
1
96
90
80
95
99.84
93.6
83.2
98.8
0
0
1
32
96
96
95
64
99.84
99.84
98.8
66.56
3
1
5
36
93
95
91
60
96.72
98.8
94.64
62.4
6
0
5
50
90
96
91
46
93.6
99.84
94.64
47.84
Figure 27: Canopy Cover Percentage along High Density Transect.
Averages were calculated from the raw data collected in the field at each point (Figure
28). To do this, the north, east, south, and west facing readings in each quadrant were averaged
together. Then, the four quadrants that make up each point were averaged. The values for each
point in each of the three densities were than averaged for comparison to other variables as well
as a total average for the entirety of each transect.
22 | P a g e
1.04
Point 1
Point 2
Point 3
Point 4
Point 5
Point 6
Point 7
Total Average
Low Density (%)
11.83
20.48
24.83
3.38
22.10
0.46
64.42
21.07
Medium Density (%)
2.93
43.75
74.17
18.14
67.67
18.72
49.60
39.28
High Density (%)
40.56
84.89
79.82
96.79
94.06
67.86
89.31
73.25
Figure 28: Canopy Cover Percentage averages for each point throughout study site.
From the numerical results of the averages, it can be interpreted that as the density of the
redcedar increased, so did the canopy cover. As a result, this variable became the deterministic
variable that can potentially be used to predict other biotic and abiotic traits of the forested area.
23 | P a g e
Daubenmire Quadrat Method for estimating understory cover.
Cover Classes:
1=1-5
2 = 6 - 25
(2.5%) 3 = 26 - 50% (37.5%) 5 = 76-95% (85%)
(15%) 4 = 51 - 75% (62.5%) 6 = 96-100% (97.5%)
Site location: 7100 Tuttle Creek Blvd, Manhattan
Scribe Name: Debbie Mildfelt
Plot # Low Density
Date:
4/20/13
Photo Numbers
Start time: 4:45pm
End time: 6:05pm
Temp: 65 degrees and overcast
Technique for worm collection: ½ canister of dry mustard to 1 gallon of water. Remove grass/turf by hand of lower right quarter of
quadrat then soak soil with mustard mixture
LIFEFORMS
plots
North
Grasses And Grasslikes (sedges and rushes)
Forbs (Herbs/broadleaf non-grasses)
Shrubs and Vines
Redcedar (From densitometer average in N,E,S & W)
directions)
Bare ground
Litter (Ground and Standing)
Persistent Litter
Rock
Moss
2
3
4
5
6
7
5
5
4
5
1
5
1
3
1
3
1
1
2
2
1
2
1
1
3
1
1
1
1
2
1
Anything else you decide to include
# of Ants
# of Teenie tiny snails
# of Worms
Sunny/shady?
South
1
1
16
1
Very
shady
Sunn
y
1
Part
sun
Most
ly
sunn
y
Part
sun
Part
sun
1
3
1
1
7
1
Most
ly
sunn
y
Notes: The grass/turf was easily removed at quadrats 2, 5, 7 (the only locations where worms were
detected). Teenie tiny snails were detected by accident at quadrats 6, 7 so were counted. These
quadrats (6,7) were close to dead trees. One ant detected at 6 so counted that in data too. The soil
was damp and quite cool, probably too cool for worms to be active at surface.
Figure 29: Shows results from mustard dilution study at Low Density
24 | P a g e
Daubenmire Quadrat Method for estimating understory cover.
Cover Classes:
1=1-5
2 = 6 - 25
(2.5%) 3 = 26 - 50% (37.5%) 5 = 76-95% (85%)
(15%) 4 = 51 - 75% (62.5%) 6 = 96-100% (97.5%)
Site location: 7100 Tuttle Creek Blvd, Manhattan
Scribe Name: Debbie Mildfelt
Plot # Medium Density
Date:
4/25/13
Photo Numbers
Start time: 3:05pm
End time: 4:45pm
Temp: 70 degrees and sunny/windy
Technique for worm collection: ½ canister of dry mustard to 1 gallon of water. Remove grass/turf by hand of lower right quarter of
quadrat then soak soil with mustard
LIFEFORMS
plots
North
Grasses And Grasslikes (sedges and rushes)
Forbs (Herbs/broadleaf non-grasses)
Shrubs and Vines
Redcedar (From densitometer average in N,E,S & W)
directions)
Bare ground
Litter (Ground and Standing)
Persistent Litter
Rock
Moss
Anything else you decide to include
# of Spider
# of Teenie tiny snails
# of Worms
2
3
4
5
6
7
3
2
1
2
1
1
1
1
1
1
2
1
2
3
2
4
3
1
1
1
3
1
2
1
1
4
1
11
9
Very
Shad
y
Part
sun
1
3
1
2
1
1
3
2
1
8
Sunny
Sunny/shady?
South
1
Very
Shady
Part
sun
2
2
7
1
4
1
Mostl
y
sunn
y
Mostl
y
shade
Notes: Quadrat 2 had 2 inches of persistent pine needles on soil. Normally, 1/3 gallon of mixture
would be poured at a time to allow liquid to soak into soils but this quadrat was so dry, it took the
entire gallon without pooling and soaked in immediately. The soil at this quadrat was extremely dry. It
also had a live juniper branch touching the soil. Quadrat 4 had grass clumps that were smashed into
the soil. Vegetation at quadrat 5 was difficult to remove. Quadrat 6 was very rocky.
Figure 30: Shows results from mustard dilution study at Medium Density
25 | P a g e
Daubenmire Quadrat Method for estimating understory cover.
Cover Classes:
1=1-5
2 = 6 - 25
(2.5%) 3 = 26 - 50% (37.5%) 5 = 76-95% (85%)
(15%) 4 = 51 - 75% (62.5%) 6 = 96-100% (97.5%)
Site location: 7100 Tuttle Creek Blvd, Manhattan
Scribe Name: Debbie Mildfelt
Plot # High Density
Date:
4/28/13
Photo Numbers
Start time: 9:35am
End time: 10:45am
Temp: 60 degrees and sunny
Technique for worm collection: ½ canister of dry mustard to 1 gallon of water. Remove grass/turf by hand of lower right quarter of
quadrat then soak soil with mustard
LIFEFORMS
plots
North
1
Grasses And Grasslikes (sedges and rushes)
Forbs (Herbs/broadleaf non-grasses)
Shrubs and Vines
Redcedar (From densitometer average in N,E,S & W)
directions)
Bare ground
Litter (Ground and Standing)
Persistent Litter
Rock
Moss
South
2
3
2
1
4
5
6
7
2
3
1
1
2
1
1
1
1
1
5
1
2
1
1
1
1
5
1
1
3
1
2
1
1
2
1
1
1
2
1
4
1
2
1
5
1
1
1
Most
ly
Shad
y
Most
ly
Shad
y
Most
ly
Shad
y
Anything else you decide to include
4
# of Teenie tiny snails
# of Worms
Very
Shady
Sunny/shady?
Mostl
y
Shad
y
3
3
Very
Shad
y
Most
ly
Shad
y
Notes: Quadrats 1, 3, 6 had pine needles 2 inches thick on the ground. Quadrat 5 had a live
juniper limb touching ground. Quadrats 3 and 4 had 3 good sized worms come to the surface
almost immediately.
Figure 31: Shows results from mustard dilution study at High Density
Canopy Cover and Understory Analysis
In order to analyze the data collected by those involved with the undercover and canopy
cover study, each variable was compared externally in a basic scatter plot. The data collected
regarding canopy cover was used as the independent variable for each graph as the transects
were divided depending on the team’s visual prediction of density. As mentioned above, the
26 | P a g e
canopy cover correlated in a positive linear direction as the density increased, suggesting that in
any spot at the study site, if the density increases, the canopy cover will increase. In order to
further predict any other trends relating to undercover and canopy cover, the following
comparisons were made and analyzed: canopy vs living matter, canopy vs dead matter, canopy
vs worms, canopy vs insects, canopy vs pH.
Figure 32: Canopy vs Living Matter scatterplot
From the above graph (Figure 32), three clusters can be generally identified relating to
low, medium, and high density. Some overlap does clearly exist, suggesting that there will not be
an absolutely true linear relationship between these two variables. Nonetheless, this point by
point analysis of canopy cover and living matter does support there being a significant difference
between the three densities and living matter.
27 | P a g e
Figure 33: Canopy vs Dead Matter scatterplot
In the above graph (Figure 33), similar trends that group dead matter into three distinct
groups exist, but are not as clearly clustered as the previous figure. Much more overlap occurred
between the 40-60% range, suggesting that is a random point in the study site with canopy cover
in that section was selected, there would not be a strong likelihood of correctly predicting the
dead matter.
28 | P a g e
Figure 34: Average Canopy vs Average Undercover scatterplot
To better understand any possible relationship between canopy cover and undercover, the
average values for these two categories were plotted against each other. As seen from Figure 34,
dead mater generally increased as canopy cover increased (R2 = 0.4093) while living matter
generally decreased as canopy cover increased (R2 = 0.6397). These two relationships make
sense in the idea that as canopy cover increases, less light will be able to penetrate to the ground
level and more litter will be produced by the surrounding plants. This data should be interpreted
with caution, however, as only three transects were used. If more data sites could have been
used, a better prediction of undercover may be determined based on canopy cover.
29 | P a g e
Figure 35: Canopy vs Worm Count scatterplot
Generally speaking, it is difficult to interpret whether or not a linear correlation exists
between canopy cover and worms from the above graph (Figure 35).
Figure 36: Average Canopy vs Average Worm count scatterplot
Once the averages were considered in Figure 36, it appears that the medium density did
not follow the established linear relationship for the study site. There does tend to be a possible
30 | P a g e
parabolic relationship between the three points, but without an increase in data sites, such a
thought is not conclusive.
Insects
Canopy vs Insects
18
16
14
12
10
8
6
4
2
0
Low
Medium
High
0
20
40
60
80
100
120
Canopy Cover (%)
Figure 37: Canopy vs Insect Count scatterplot
Figure 37 above again shows a lack of clustering – particularly in the medium density
range. From this chart alone, the high density did seem to produce the lowest amount of insects
at each point, but the low density also seemed to provide this trend.
31 | P a g e
Figure 38: Average Canopy vs Average Insect Count scatterplot
From Figure 38 above, the thought that little correlation exists is confirmed. There is not
a linear relationship between insects and canopy cover, suggesting that if the canopy cover is
known for a given spot, the number of insects present cannot be predicted.
pH
Canopy vs pH
8.15
8.1
8.05
8
7.95
7.9
7.85
7.8
7.75
7.7
7.65
Low
Medium
High
0
20
40
60
80
100
120
Canopy Cover (%)
Figure 39: Canopy vs pH scatterplot
32 | P a g e
No major results can be interpreted from the above figure (Figure 39) as no significant
clustering or regression seems to occur within any of the three densities. Of the later variables
analyzed in the study however, pH may be of interest for further statistical analysis. While this
study focused on the three different densities, it might be more accurate to ignore initial density
identification and instead solely consider canopy cover at each point. Doing so may reveal a
general decrease in pH as canopy cover increases, but this idea was outside of this study’s scope
of work.
Figure 40: Average Canopy vs Average pH scatterplot
Figure 40 above proves that a linear relationship between canopy cover and pH does not
exist. However, one idea does seem to present them from the above graph. There may be a
threshold at which the pH drops in the soil depending on the canopy percentage. This idea would
likely be supported or rejected if the above mentioned analysis would be completed regardless of
initial density identification.
33 | P a g e
Biomass Analysis
Linear regression analysis was used to examine the relationships between the key
variables and redcedar biomass. Because the different groups recorded the data using different
units of measurement, conversion of some measurements had to occur to ensure uniformity in
order to run an accurate regression model. This table is shown below.
Density/
Plot/Quad
Circumference
Ground Line
(m)
Circumference
Breast Height
(m)
Distance from
Center of Plot
(m)
Canopy
Width
(m)
Tree
Height
(m)
Weight
(lbs)
Medp1q1
0.762
0.51
4.57
4.4
6.4
128
Medp1q2
0.432
0.23
7.13
2.5
3.2
Medp1q3
0.457
0.31
10.9
3
3.6
Medp1q4
1.02
0.69
5.03
5.7
5.6
615
Medp4q1
0.94
0.686
3.35
4.1
5.2
522
Medp4q2
0.41
0.152
2.74
2.7
3.6
Medp4q3
0.483
0.28
4.88
2.9
4.4
Medp4q4
0.991
0.584
6.71
3.9
5.6
Medp7q1
1.041
1.321
9.14
7.1
7.6
Medp7q2
1.321
0.838
4.82
4.7
7.6
Medp7q3
0.56
0.406
4.8
3.8
4.8
Medp7q4
0.28
0.508
5.61
2.9
3.6
Lowp1q1
1.43
1.08
7.6
8.48
6.54
Lowp1q2
0.9
0.59
13.65
4.24
7.23
Lowp1q3
0.9
0.8
22.9
7.63
6.64
Lowp1q4
1.22
0.63
5
6.25
5.7
142
247
34 | P a g e
Lowp2q1
1.35
0.85
10.8
7.83
6.8
Lowp2q2
0.37
0.21
3.47
3.3
3.3
Lowp2q3
0.62
0.4
14
3.85
4.2
Lowp2q4
0.42
0.25
7.2
2.65
4.39
Lowp3q1
1.35
1
9
8.48
6.12
Lowp3q2
1.3
0.56
24.23
7.2
6.54
Lowp3q3
0.78
0.53
5.45
3.95
5.78
Lowp3q4
0.82
0.36
5.95
5.9
5
After this, two summary tables were produced; one showing the means of the
measurements by plot, the other showing the means of the measurements by transect (Figures 40
and 41, respectively), and a chart was produced showing the means of the variables for the two
transects in which the measurements were taken (Figure 42).
Figure 40 (above) shows the means of the key variables by plot; while figure 33 (below) summarizes the means of the
key variables by transect.
35 | P a g e
12
10
8
6
Medium Density
Low Density
4
2
0
Mean CGL (m) Mean CBH (m) Mean Distance Mean Canopy
from Center (m) Width (m)
Mean Tree
Height (m)
Figure 42 (above) is a chart that displays the means of the key variables in a manner which makes it easy to compare the
differences between the medium and low transects. The variables distance from center plot, canopy width, and tree height
show the greatest difference.
Before running the regression, the data needed to be checked for multicollinearity. It was
expect that a high level of multicollinearity would be observed due to the nature of the variables,
most of which are inherently related to one another. A correlation matrix was produced in order
to test the chosen variables for multicollinearity, shown below. All variables were included in the
matrix.
36 | P a g e
Figure 42: Multicollinearity matrix
As can be seen in the correlation matrix above, there was indeed a very high level of
multicollinearity among most of the variables. Because of this, it was determined to not run a
multivariate regression model, but rather to test each key variable individually to see which
variables are the best predictors of biomass. To do this, a series of simple bivariate regression
models were run, testing each key variable as the explanatory variable against the response
variable of tree weight.
Variable
R-squared value
Circumference Ground Line (m)
0.719
Circumference Breast Height (m)
0.7343
Canopy Width (Average)(m)
0.4996
Tree Height (m)
0.0063
Figure 43: R-squared values for biomass variables
37 | P a g e
Circumference at Ground Line
800
700
y = 817.41x - 284.71
R² = 0.7192
Tree Weight
600
500
400
Weight (lbs)
300
200
100
0
0
0.5
1
1.5
CGL (in meters)
Figure 44: Circumference at ground level vs Tree Weight scatterplot
Circumference at Breast Height
1400
1200
y = 1078.4x - 223.91
R² = 0.7343
Tree Weight
1000
800
600
Weight (lbs)
400
200
0
-200
0
0.5
1
1.5
CBH (in meters)
Figure 45: Circumference at Breast Height vs Tree Weight scatterplot
38 | P a g e
Canopy Width
900
800
y = 155.64x - 319.76
R² = 0.4996
Tree Weight
700
600
500
Weight (lbs)
400
300
200
100
0
0
2
4
6
8
Canopy Width (in meters)
Figure 46: Canopy Width vs Tree Weight scatterplot
Tree Height
700
600
y = 23.176x + 208.43
R² = 0.0063
Tree Weight
500
400
Weight (lbs)
300
200
100
0
0
2
4
6
8
Tree Height (in meters)
Figure 47: Tree Height vs Tree Weight scatterplot
As can be seen from the table and graphs above, there was a wide range of r-squared
values, with circumference at breast height (CBH) being the highest and tree height the lowest.
39 | P a g e
In other words, CBH is the best predictor of redcedar biomass, followed by circumference at
ground line (CGL), canopy width, and tree height being the worst predictor. Given the small
sample size (only five tree weights were used), there was some worry about the accuracy of these
results. To better ensure accuracy, it was desired to test the results using a model with a larger
sample size. To do this, data recorded from a similar study in a previous NRES capstone class
was utilized. This data is shown below. Again, some measurement units needed to be converted
for uniformity purposes.
Diameter at Breast Height (in) Weight
5
490
3.6
70
6.4
340
5.3
280
9
960
3.8
135
11.3
1271
2.5
62
6.13
505
1.88
32
4.75
235
12.13
2130
6.29
128
8.64
615
8.59
522
3.5
142
5.079
247
Figure 48: Diameter at Breast Height and Weight table
Because the 2013 group and the group from the previous study took different
measurements, only the variable diameter at breast height (the measurements were converted
from circumference to diameter) was used, because it was common to both studies and it yielded
the greatest correlation to biomass in this study.
40 | P a g e
Diameter at Breast Height
2500
Tree Weight
2000
y = 163.36x - 518.08
R² = 0.7901
1500
1000
Weight
500
0
0
5
10
15
-500
DBH (in inches)
Figure 49: Diameter at Breast Height vs Tree Weight scatterplot
As expected, with the increased sample size of 17, the r-squared value increased, going
from 0.7343 to 0.7901. These results led us to conclude there is a fairly strong correlation
between diameter/circumference at breast height and biomass in Eastern Redcedar trees.
41 | P a g e
Further Study
Further study shows the by-products from Eastern Redcedar can be marketed for profit
by landowners, businesses and communities. The abundance of Juniperus virginiana across
much of the Great Plains has sparked interest in many individuals and organizations to find a
way to maximize the economic value of harvesting this tree. One interest is to process the
redcedar into ethanol bio-fuel which could then be used as an alternative renewable energy
source. While this proposal sounds promising, there are concerns on how to effectively harvest,
transport, and process these trees in a way to make a steady profit. One processing technique is
to heat redcedar chips to extract the cedar oils and then convert the remaining biomass into either
jet fuel or diesel (McNutt 2012). Residue, after extraction can be used as boiler fuel for
regenerating the steam for the oil process and as space heating in the winter (Gold et al. 2005).
According to (McNutt 2012) 200 gallons of jet fuel can be produced per one ton of dry biomass
and the cedar oils extracted can be sold for approximately $45 per gallon. As a result,
nationwide, Eastern Redcedar is an estimated $60 million dollar per year industry (Gold et al.
2005).
The developing research is to locate a practical area in which an industrial plant can be
constructed to process the oil and fuel from huge volumes of redcedar stands. Estimating the
biomass of these stands is going to be essential in deciding where to harvest. The ground-based
sampling method used in this study shows that there are associations to estimate above ground
biomass. Although, large-area inventory of redcedar mass can only be practically addressed via
aircraft or satellite remote sensing, because it is too costly, time consuming, and labor intensive
to produce such inventories via ground-based sampling (Starks et al. 2011). Satellite and aerial
42 | P a g e
imagery can be used more effectively to determine which tree density has the maximum biomass
yield; low, medium or high.
43 | P a g e
Conclusion
From the data collect at the study site and the analysis completed throughout this report,
multiple conclusions can be made regarding Red Cedar biomass and canopy/understory cover.
First, trends were identified between canopy cover and initially identified density. As the
densities of the transects increased, so did the canopy cover. This relationship, while possibly
elementary, is a key component in further research relating to the Red Cedar.
Second, as canopy cover increased, the living matter in the understory decreased while
the dead matter in the understory increased. This trend was concluded with an R2 of 0.6397 and
0.4093 for living and dead matter, respectively. As a result, if a point has a known canopy cover
percentage, the understory composition can be predicted with accuracy between 40-63% in this
study site.
Third, no significant relationships existed between soil pH, worm, or insect content and
density. Whether the density was low, medium, or high, these three dependent variables did not
show significant change. Possible regression analysis could be conducted to further solidify this
conclusion.
Lastly, it can be concluded that the best predictor of Red Cedar biomass among the key
variables is circumference at breast height. Circumference at ground line, with an r-squared
value of 0.7192, also has a decently strong correlation. Canopy width, at 0.4996, has a weak
correlation; and tree height, at 0.0063, has virtually no correlation to biomass.
44 | P a g e
Additional Images
Figure 50: The above image shows the two groups setting up the medium density transect.
Figure 51: The below image on the left is showing to group members laying out the low density transect.
Figure 52: The below image on the right is showing a group member taping out 30 ft. plots on a transect.
45 | P a g e
Figures 53-56 display the weighing process for Redcedars conducted in this study
46 | P a g e
Figure 57: The above image showls a team member getting ready to pour a mustard mixture on the ground and wait for
worms to surface.
Figure 58: The below image shows team member clearing ground cover to check for worms.
47 | P a g e
References
Alford, Aaron L. et al. “Experimental Tree Removal in the Tallgrass Prairie: Variable
Responses of Flora and Fauna Along a Woody Cover Gradient” Ecological Applications.
Vol 22. April 2012
Fitch, Henry S. et al. “A Half Century of Forest Invasion on a Natural Area in Northeast
Kansas.” Transactions of the Kansas Academy of Science vol 104. April 2001
Gold, M.A., L.D. Godsey., and M.M. Cernusca. 2005. Eastern Redcedar Market Analysis.
University of Missouri Center for Agroforestry, Columbia, MO. Pp. 46
Hansen, Jeff D. 2012. Ecoregions of Kansas. Kansas Native Plant Society (KNPS).
http://www.kansasnativeplantsociety.org/ecoregions.php
Horncastle, Valerie J., Eric C. Hellgren, Paul M. Meyer, Amy C. Ganguli, David M. Engle, and
David M. Leslie Jr. “Implications of invasion by Juniperus virginiana on small mammals
in the southern Great Plains.” Journal of Mammalogy 86(6): 1144-1155, 2005.
McKinley, Craig R. 2012. “The Oklahoma Redcedar Resource and Its Potential Biomass
Energy.” Oklahoma Cooperative Extension Service. NREM-5054:1-4
McNutt, Michael. 2012. “Oklahoma looks at ways to curb Redcedar trees.” Newsok.com. The
Oklahoman. Sept. 2. Web
Norris, Mark D., John M. Blair, and Loretta C. Johnson. (2001). “Land cover change in eastern
Kansas: litter dynamics of closed-canopy eastern Redcedar forests in tallgrass prairie.”
Canadian Journal Of Botany, 79(2), 214-222.
Norris, Mark D., John M. Blair, Loretta C. Johnson, and Robert B. McKane (2001). “Assessing
changes in biomass, productivity, and C and N stores following Juniperus viriniana forest
expansion into tallgrass prairie.” Canadian Journal Of Forest Research, 31(11), 2001.
Pierce, Ann M. and Peter B. Reich. “The effects of Eastern Redcedar (Juniperus virginiana)
invasion and removal on a dry bluff prairie ecosystem.” Biological Invasions 12(1): 241252, July 2008.
Starks, P. J., B.C. Venuto, J.A. Eckroat and Tom Lucas. 2011. Measuring Eastern Redcedar
(Juniperus virginiana L.) Mass With the Use of Satellite Imagery. Rangeland Ecology
and Management. 64(2):178-186.
48 | P a g e